Principal AI Engineer

About the role

Principal AI Engineer

Location: New York City (Hybrid: 2–3 days on-site)

Employment Type: Full-Time Employment

Experience Level: Staff/Principal (8–14 years)

About the Role

Turing is hiring a Staff/Principal AI Engineer to lead enterprise-scale agentic AI implementations for Fortune 500 clients. This is a hands-on engineering role focused on designing and shipping autonomous, tool-calling AI systems—agents that reason over enterprise context, invoke real systems through secure interfaces, and operate reliably at scale under strict latency, cost, and governance constraints.

You will own these systems end to end: the user-facing applications, data pipelines, backend services, agent orchestration layer, evaluation harness, and cloud infrastructure. Strong full-stack coding ability is a mandatory requirement for this role. Candidates must be comfortable building production applications across the frontend, backend, data, and infrastructure layers—not solely developing models, prompts, notebooks, or proofs of concept.

We are looking for engineers with genuine software engineering and data science depth who have taken agentic systems all the way to production.

Roles & Responsibilities

Design and build agentic systems: Lead the architecture and implementation of tool-calling agents that combine retrieval, structured reasoning, and secure action execution with least-privilege access.

Productionize LLM applications: Build retrieval pipelines, prompt synthesis, response validation, and self-correction loops backed by rigorous evaluation.

Own the full application stack: Build production-grade frontend applications and user experiences, backend APIs and services, data pipelines, databases, distributed compute, and agent orchestration. This is a hands-on coding responsibility across the full stack.

Engineer for reliability and governance: Build validator models, adversarial test suites, and policy checks; enforce deterministic fallbacks and rollback strategies; instrument continuous evaluation.

Optimize for cost and latency: Drive measurable improvements in token efficiency, response time, and unit economics against defined SLOs.

Codebase ownership: Build, maintain, test, and review high-quality production code, with an emphasis on reusable components, scalability, security, and performance.

Cloud integration: Deploy AI applications on AWS, Azure, or GCP with optimized resource usage and robust CI/CD.

Cross-functional collaboration: Partner with product owners, data scientists, and business SMEs to define requirements and deliver impactful AI products.

Mentoring and technical leadership: Set engineering standards and share knowledge across the team, raising the bar for AI and software engineering practices.

What We’re Looking For

Full-Stack Engineering — Mandatory

Proven ability to independently build and ship complete, production-grade applications across the frontend, backend, data, and cloud infrastructure layers.

Strong hands-on experience with modern frontend development using React, Angular, or Vue, along with JavaScript or TypeScript, HTML, and CSS.

Strong backend development experience using Python and APIs, microservices, authentication, databases, and asynchronous or distributed processing.

Experience integrating LLM and agentic capabilities into user-facing applications, including streaming responses, tool execution, state management, error handling, and observability.

Strong experience with relational and/or NoSQL databases, cloud deployment, containers, testing, and CI/CD.

Candidates whose experience is limited to data science, model development, prompt engineering, notebooks, or backend-only AI prototypes will not be a fit.

Engineering Foundation

8–14 years of software engineering experience, with strong hands-on, large-scale Python development.

Working depth in at least one systems or backend language—Go, Rust, Java, or C/C++—and the judgment to know when to use it.

Strong knowledge of data structures and algorithms.

Strong understanding of APIs, microservices, and system design.

Hands-on experience building and operating data pipelines and production-grade distributed systems.

Agentic AI and LLMs

2+ years of hands-on LLM engineering, including at least two agentic systems that you designed and took to production.

Production experience with agent frameworks such as LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent, with the fluency to move between frameworks as the ecosystem evolves.

Experience building MCP (Model Context Protocol) servers and tool-calling interfaces.

RAG from first principles: chunking strategy, embeddings, vector and hybrid retrieval, reranking, and response validation.

Strong experience with vector databases such as Milvus, Pinecone, Weaviate, FAISS, or cloud equivalents.

Experience designing guardrails and reliability patterns, including validators, policy checks, self-correction loops, deterministic fallbacks, circuit breakers, and rollback paths.

Optimization

Deep familiarity with token optimization and context-window management, including context shaping, pruning, and compaction.

Latency and cost optimization through caching, model routing, batching, streaming, and parallel tool calls.

Experience testing and tuning system performance against defined SLOs.

Evaluation

Experience building evaluation frameworks for LLM systems, including offline evaluation sets, continuous online evaluation, and regression detection.

Instrumentation and traceability suitable for regulated enterprise environments using tools such as LangSmith, Langfuse, or equivalent.

Cloud

Hands-on AWS experience, including containerized services such as ECS/EKS, serverless services such as Lambda, data services such as S3, DynamoDB, and Redshift, and orchestration through Step Functions. Azure or GCP equivalents are also valued.

Familiarity with CI/CD pipelines and mature DevOps practices.

Infrastructure as code using Terraform or CloudFormation.

Working Traits

Strong analytical problem-solving skills with a bias toward ownership and urgency.

Clear cross-team and client-facing communication, with the ability to translate business problems into technical roadmaps.

Ability to work productively through ambiguity, understand system-level documentation, and ramp quickly in unfamiliar codebases.

Good to Have

Experience with managed AI platforms such as Amazon Bedrock, Vertex AI, or Azure AI, paired with fluency in the underlying fundamentals.